The first decentralized AI model from the ASI Alliance, Cortex, is aimed at domain-specific robotics, biotechnology, and healthcare applications.
Cortex, a decentralized AI model created to meet industry difficulties, was introduced by the Artificial Superintelligence ASI Alliance, a cooperative organization dedicated to promoting the development of decentralized artificial intelligence.
The “ASI: Train initiative,” which aims to develop domain-specific AI solutions for sectors including robotics, biotechnology, and healthcare, has introduced Cortex as its first model.
In contrast to general-purpose AI models, Cortex is made to be precise, scalable, and flexible enough to accommodate the unique requirements of different sectors. It emphasizes tasks that need sophisticated, situation-specific problem-solving.
According to Fetch.ai CEO and ASI Alliance chairman Humayun Sheikh, this advancement could lessen the need for centralized AI solutions by enabling organizations to “train, own, and build solutions with decentralized AI-trained models.”
Meeting the demands of a particular industry
With its high relevance in manufacturing, autonomous delivery, and data-driven research, Cortex is intended to offer a substitute for the general-purpose AI models now in use.
The partnership has “several partners willing and able to use the Cortex model for inferences,” Sheikh said, adding that the robotics sector is an “exciting area at the moment.”
“Biotechnology, healthcare, gig economy, and research. All these areas require data coming from various research organizations, which could, in a combined way, result in step changes in discovery and utilization.”
Decentralized ownership of AI
By utilizing governance-based models to offer an alternative to centralized AI solutions, Cortex will use a decentralized framework to promote cooperation among diverse industries and organizations.
Its decentralized structure seeks to spread the advantages of AI development to prevent power concentrations that impede developers’ ability to innovate freely.
The ASI: Train initiative’s initial launch could provide particular businesses with a more inclusive choice for collaborative AI development catered to their unique requirements by overcoming the drawbacks of centralized AI systems.
Proto-AGI self-learning in Minecraft
Together with the decentralized AI network SingulatirtyNET, the ASI Alliance recently introduced the first self-learning proto-AGI in Minecraft.
The development potential for artificial general intelligence (AGI) may be enhanced by the new proto-AGI, known as Autonomous Intelligent Reinforcement Inferred Symbolism (AIRIS).
By using the new AI technology in Minecraft, the alliance can observe how the proto-AGI learns and adapts independently, expanding the possibilities of AI in robotics, automation, and intelligent systems intended to address real-world problems.
